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Bansal, Mohit (14)
The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (7)
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., Ramakanth (2)
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Hits 1 – 14 of 14
1
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bansal, Mohit
;
Guo, Han
. - : Underline Science Inc., 2021
BASE
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2
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bansal, Mohit
;
Bauer, Lisa
. - : Underline Science Inc., 2021
BASE
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3
EmailSum: Abstractive Email Thread Summarization ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Celikyilmaz, Asli
. - : Underline Science Inc., 2021
BASE
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4
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bansal, Mohit
;
Jiang, Yichen
. - : Underline Science Inc., 2021
BASE
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5
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Kiela, Douwe
. - : Underline Science Inc., 2021
BASE
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6
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Chang, Shih-Fu
. - : Underline Science Inc., 2021
BASE
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7
Continuous Language Generative Flow ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Kim, Hyounghun
. - : Underline Science Inc., 2021
BASE
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8
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Frey, Benjamin
. - : Underline Science Inc., 2021
BASE
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9
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bansal, Mohit
;
Maharana, Adyasha
. - : Underline Science Inc., 2021
BASE
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10
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Ramakanth
;
Bansal, Mohit
. - : Underline Science Inc., 2021
BASE
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11
Continual Few-Shot Learning for Text Classification ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Ramakanth
;
Bansal, Mohit
. - : Underline Science Inc., 2021
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12
Finding a Balanced Degree of Automation for Summary Evaluation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bansal, Mohit
;
Zhang, Shiyue
. - : Underline Science Inc., 2021
BASE
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13
Analysis of Tree-Structured Architectures for Code Generation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bansal, Mohit
;
Dahal, Samip
;
Maharana, Adyasha
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.384 Abstract: Code generation is the task of generating code snippets from input user specifications in natural language. Leveraging the linguistically-motivated hierarchical structure of the input can benefit code generation, especially since the specifications are complex sentences containing multiple variables and operations over various data structures. Moreover, recent advances in Transformer architectures have led to improved performance with tree-to-tree style generation for other seq2seq tasks e.g., machine translation. Hence, we present an empirical analysis of the significance of input parse trees for code generation. We run text-to-tree, linearized tree-to-tree, and structured tree-to-tree models, using constituency-based parse trees as input, where the target is Abstract Syntax Tree (AST) of the code. We evaluate our models on the Python-based code generation dataset CoNaLa and a semantic parsing dataset ATIS. We find that constituency ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/26475-analysis-of-tree-structured-architectures-for-code-generation
https://dx.doi.org/10.48448/yhxr-pm37
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14
Simple, accurate parsing with an all-fragments grammar
Bansal, Mohit
;
Klein, Dan
In:
Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL
48 (2010) 2, 1098-1107
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